Image registration is the act of spatially mapping the coordinate system of one image to the coordinate system of another image. Registration techniques, for example, can be useful in medical procedures in which a pre-operative image space needs to be properly correlated to a real-time physical space.
Automatic registration algorithms generally consist of three components: A similarity metric for measuring the correspondence between the images (e.g.: cross correlation, mutual information; A set of allowable transformations that can be applied to one image in order to match it to the other (e.g.: rigid, affine, free-form); and. A method for searching the space of allowable transformations to find the optimal transform as the solution (e.g.: gradient descent, stochastic gradient descent, Powell's method, least squares).
Automatic registration methods perform adequately in well-posed cases, but these methods experience difficulty in routine clinical use. Challenges are introduced by pathology, imaging artifacts, and differences in image acquisition.
Rigid registration methods find a correspondence between two images by seeking to maximize a similarity metric computed globally (over a large span of the image, if not its entirety). These global measures encounter difficulty in clinical settings when the field of view of one image does not encompass the entire field of view of the other. This challenge is more pronounced when the similarity metric is mutual information (useful for registering T1-weighted MRI to T2-weighted MRI, or MRI to CT), as opposed to correlation. In these cases, correspondence can be accurately computed locally, but the global discrepancies cause greedy search algorithms to converge to local minima. The search algorithms in medical applications are typically greedy, referring to the method of searching a solution space by following gradient descent, or a similar deviant such as stochastic gradient descent. The reason for this is that the images are very large, and the applications must meet stringent clinical demands for speed.
Non-rigid registration methods typically begin with a rigid or affine registration step, followed by a more refined registration that corrects local misalignments. The localized warping attempts to align the anatomical structures, but there exists ambiguity between whether an observed intensity difference between images is caused by a difference in positioning of anatomy, or other factors. These other factors include imaging artifacts, contrast uptake, and pathology.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for an automatic registration method that overcomes the shortcomings of the prior art. There is also a need for improved registration that allows the user of an automatic registration system to input information during the registration process.